{"id":"W2040525714","doi":"10.1109/sapiw.2013.6558343","title":"Capacitance extraction in lossy layered substrates via Barnes-Hut accelerated utilizing per-layer center-of-charge (CoC)","year":2013,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Lossless compression; Lossy compression; Capacitance; Extraction (chemistry); Charge (physics); Center (category theory); Generalization; Physics; Algorithm; Materials science; Analytical Chemistry (journal); Topology (electrical circuits); Computer science; Optoelectronics; Combinatorics; Mathematics; Data compression; Chemistry; Artificial intelligence; Crystallography; Mathematical analysis; Quantum mechanics; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002289557,0.0005093429,0.0003457585,0.0003873495,0.0004254271,0.0007285207,0.0009299204,0.0003675892,0.005421048],"category_scores_gemma":[0.001239382,0.0002500875,0.0002339829,0.0008266714,0.0002861019,0.0009221486,0.0004601541,0.0003991122,0.001006954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000706476,"about_ca_system_score_gemma":0.000943706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005392945,"about_ca_topic_score_gemma":0.01227226,"domain_scores_codex":[0.9998772,0.00002060095,0.000004183625,0.000009476567,0.00006887013,0.00001964797],"domain_scores_gemma":[0.9995093,0.0002326609,0.00004791998,0.00007466969,0.0001124344,0.00002291097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004490052,0.0001009551,0.004028288,0.0003479993,0.0001026681,0.0006748793,0.0003751583,0.6062902,0.05494908,0.05750662,0.009825585,0.2653495],"study_design_scores_gemma":[0.00001464323,0.00002401456,0.0002498059,0.000008055861,0.000006372176,0.00007970764,0.00002959605,0.9809933,0.01253089,0.003713483,0.002340037,0.00001023458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1057824,0.0002313042,0.8724235,0.0001778434,0.00004884135,0.00004550917,0.0002711772,0.005223582,0.01579589],"genre_scores_gemma":[0.519752,0.0001751892,0.4731759,0.000114832,0.00001809878,0.00005486041,0.0003509992,0.0006427742,0.005715346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005421048,"threshold_uncertainty_score":0.01813519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250126956372628,"score_gpt":0.2657407280716191,"score_spread":0.2407280324343563,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}